On classifiers for blind feature‐based automatic modulation classification over multiple‐input–multiple‐output channels - Archive ouverte HAL Access content directly
Journal Articles IET Communications Year : 2016

On classifiers for blind feature‐based automatic modulation classification over multiple‐input–multiple‐output channels

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Abstract

Modulation recognition is crucial for a good environmental awareness required by cognitive radio systems. In this study, the authors design and compare models of four among the most commonly used classifiers for feature-based automatic modulation classification (FB-AMC) algorithms. Classifiers whose models will be designed are classification tree, K-nearest neighbours, artificial neural networks (ANNs), and support vector machines. In this study, they apply some statistical pattern recognition techniques in the context of blind FB-AMC over multiple-input–multiple-output channels. Comparison criteria are classification accuracy and computational complexity. To improve the impartiality of this comparison, each classifier is optimally deployed by selecting its optimal model with respect to their context. Model selection for the classifiers is done using the ‘k-fold cross-validation’ model validation technique. The comparison study, within the considered context, shows that ANN classifiers have the best performance/complexity tradeoff.
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Dates and versions

hal-03382947 , version 1 (18-10-2021)

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Sofiane Kharbech, Iyad Dayoub, Marie Zwingelstein, Eric Pierre Simon. On classifiers for blind feature‐based automatic modulation classification over multiple‐input–multiple‐output channels. IET Communications, 2016, 10 (7), pp.790-795. ⟨10.1049/iet-com.2015.1124⟩. ⟨hal-03382947⟩
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